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rule_learning_margin_3mm_many_negatives_spanpred_attention
enoriega/rule_learning_margin_3mm_many_negatives_spanpred_attention
rule_learning_margin_3mm_many_negatives_spanpred_attention is a machine learning model from enoriega. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Jul 23, 2022
- Updated
- Jul 25, 2022
- SHA
- 0bf315b3
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
rule_learning_margin_3mm_many_negatives_spanpred_attention
This model is a fine-tuned version of enoriega/rule_softmatching on the enoriega/odinsynth_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.2196
- Margin Accuracy: 0.8969
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2000
- total_train_batch_size: 8000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Margin Accuracy |
|---|---|---|---|---|
| 0.3149 | 0.16 | 60 | 0.3098 | 0.8608 |
| 0.2754 | 0.32 | 120 | 0.2725 | 0.8733 |
| 0.2619 | 0.48 | 180 | 0.2512 | 0.8872 |
| 0.2378 | 0.64 | 240 | 0.2391 | 0.8925 |
| 0.2451 | 0.8 | 300 | 0.2305 | 0.8943 |
| 0.2357 | 0.96 | 360 | 0.2292 | 0.8949 |
| 0.2335 | 1.12 | 420 | 0.2269 | 0.8952 |
| 0.2403 | 1.28 | 480 | 0.2213 | 0.8957 |
| 0.2302 | 1.44 | 540 | 0.2227 | 0.8963 |
| 0.2353 | 1.6 | 600 | 0.2222 | 0.8961 |
| 0.2271 | 1.76 | 660 | 0.2207 | 0.8964 |
| 0.228 | 1.92 | 720 | 0.2218 | 0.8967 |
| 0.2231 | 2.08 | 780 | 0.2201 | 0.8967 |
| 0.2128 | 2.24 | 840 | 0.2219 | 0.8967 |
| 0.2186 | 2.4 | 900 | 0.2202 | 0.8967 |
| 0.2245 | 2.56 | 960 | 0.2205 | 0.8969 |
| 0.2158 | 2.72 | 1020 | 0.2196 | 0.8969 |
| 0.2106 | 2.88 | 1080 | 0.2192 | 0.8968 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.1
- Tokenizers 0.12.1
- Library
- transformers
- Type
- bert
- Created
- Jul 23, 2022
- Updated
- Jul 25, 2022